Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation

• Type: Article in International Scientific Journal
• Date: 2022-06

Authors

Mohammadamin Salimi (Author) Other; José J.M. Machado (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Sensors

Vol. 22 No. 4

Pages: 4544-4544

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Other information

Authenticus ID: P-00W-THA

DOI: 10.3390/s22124544

Abstract (EN): Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15